Jordi Vitrià

Jordi Vitrià

Jordi Vitrià
PhD in Computer Science, from Universitat Autònoma de Barcelona
Professor at at Universitat de Barcelona

Personal webpage

email: jordi.vitria@ub.edu

 

Biosketch

Dr. Jordi Vitrià is a senior researcher and full professor at UB. He received his Ph.D. degree from the Universitat Autònoma de Barcelona in 1990. Dr. Jordi Vitrià has more than 25 years of experience in working on Computer Vision and its applications to several fields. His research, when personal Computers had 128KB of memory, was originally oriented towards digital image analysis and how to extract quantitative information from them, but soon evolved towards computer vision problems. After a postdoctoral year at the University of California at Berkeley in 1993, he focused on Bayesian methods for computer vision methods. Now, he is the head of a research group working in object recognition, deep learning and image analytics. In 2007, he joined the Applied Mathematics and Analysis Department at the University of Barcelona (UB) as Full Professor, where he teaches an introductory course on Algorithms and advanced courses on Data Science and Big Data. From April 2011 He is serving as Head of the Applied Mathematics and Analysis Department, UB.

Research Interests

  • Deep Learning
  • Visual Recognition
  • Image Analytics
  • Statistical Learning.

Selected publications

  • Seguí, S.; Pujol, O.; Vitrià, J. 2015. Learning to count with deep object features. arXiv:1505.08082 [cs.CV]
  • Seguí, S.; et al. 2014. Detection of wrinkle frames in endoluminal videos using betweenness centrality measures for images. IEEE Transactions On Information Technology in Biomedicine. Institute of Electrical and Electronics Engineers (IEEE). 99, pp.1. ISSN 1089-7771
  • Seguí, S.; Igual, L.; Vitrià, J. 2013. Bagged One Class Classifiers in the Presence of Outliers. International Journal of Pattern Recognition and Artificial Intelligence. World Scientific Publishing. ISSN 0218-0014
  • Rojas, M.; et al. 2011. Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models. PLoS One. Public Library of Science (PLoS). 6 – 8. ISSN 1932-6203
  • Pujol, O., Radeva, P., and Vitria, J. “Discriminant ecoc: A heuristic method for application dependent design of error correcting output codes.”Pattern Analysis and Machine Intelligence, IEEE Transactions on 28.6 (2006): 1007-1012